Detection of Characteristic Co-Occurrence Words from News Articles on the Web
Xiao Feng, Noro Tomoya, Takehiro Tokuda · Frontiers in artificial intelligence and applications · 2012
A large number of news articles are published on the Web every day, and demand of discovering news articles on new/important topics has been growing. In this paper, we present a method for detecting characteristic words co-occurring with a target word (characteristic co-occurrence words) to help users find important topics related to the target word. The method divides news articles published in a certain period of time into two groups by whether the target word is included or not, then computes score of each word co-occurring with the target word in some news articles by counting the number of news articles including the co-occurring word for each of the news article groups. We can detect characteristic co-occurrence words more effectively by clustering news articles in advance and computing the score only in clusters which news articles including the target word belong to.